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This thesis intersects traditional operations management and data analytics with clinical applications to provide insight into the patient journey – the process of how patient seek and receive care from the healthcare system. In the first section of the thesis, we empirically analyze the beginning stage of the patient journey of opioid use. In the United States alone, the opioid epidemic contributes to tens of thousands of deaths each year, and the government has funded billions of dollars of interventions including prescription drug monitoring programs and tamper-resistant opioid formulations. As most research is focused on chronic opioid users, little is known about how operational interventions shortly after opioid initiation can impact a patient’s likelihood of long-term opioid use. We combine medical and pharmaceutical claims to investigate the care delivery process for patients who initiate opioid prescriptions in the primary care setting. Of the patients who return to the primary care setting for a follow-up appointment, we estimate the impact of provider discordance (i.e. seeing a clinician other than the original opioid prescriber for the follow-up appointment) on long-term opioid use. A series of controlled logistic regressions, instrumental variable analyses, and propensity score matching (with a bias minimization method) establish a significant causal effect in the presence of unobserved confounders. Additionally, we analyze a potential mechanism underlying the main effect and investigate the factors that could help inform a potential process intervention. The analyses provide robust evidence that provider discordance during opioid initiation could be a promising and hitherto untapped opportunity to reduce the influx of patients afflicted by the opioid epidemic. In the second section of this thesis, we take a broader view of the patient journey and develop a methodology to extract a clinically meaningful representation of patient journey diagnosis and treatment patterns from claims data. While medical and pharmaceutical claims are a rich source of data in healthcare research, they are also notoriously difficult to analyze. We first process claims into strings of characters that represent the series of events (e.g., distinct interactions with the healthcare system) in the patient journey. Then we adapt the dynamic program underlying the Levenshtein edit distance sequence alignment algorithm to evaluate the level of similarity between each pair of episodes. The similarity scores serve as inputs to an unsupervised clustering algorithm that extracts the main patterns of the patient journey. When the methodology is applied to the context of mechanical back and neck pain, it yields an overview of the main treatment patterns that had not been previously captured in the medical literature. The methodology also enables investigation into self-triage: how the patient’s first decision of where to seek care is associated with longer-term variation in treatment. We identify clear differences in patient-level socio-economic and clinical characteristics between both the choice of initial site of care and treatment patterns, highlighting the potential public health impact of this methodology. Overall, we show that applying data mining and econometrics to claims data yields insights that contribute to both the medical and operations fields.

The aim of the thesis is to investigate the feasibility of using text mining methods to reconstruct patient journeys from unstructured clinical narratives. A novel method to extract and represent patient journeys is proposed and evaluated in this thesis. A composition of methods were designed, developed and evaluated to this end; which included health-related concept extraction, temporal information extraction, and concept clustering and automated work-flow generation. A suite of methods to extract clinical information from healthcare narratives were proposed and evaluated in order to enable chronological ordering of clinical concepts. Specifically, we proposed and evaluated a data-driven method to identify key clinical events (i.e., medical problems, treatments, and tests) using a sequence labelling algorithm, CRF, with a combination of lexical and syntactic features, and a rule-based post-processing method including label correction, boundary adjustment and false positive filter. The method was evaluated as part of the 2012 i2b2 challengeand achieved a state-of-the-art performance with a strict and lenient micro F1-measure of 83.45% and 91.13% respectively. A method to extract temporal expressions using a hybrid knowledge- (dictionary and rules) and data-driven (CRF) has been proposed and evaluated. The method demonstrated the state-of-the-art performance at the 2012 i2b2 challenge: F1-measure of 90.48% and accuracy of 70.44% for identification and normalisation respectively. For temporal ordering of events we proposed and evaluated a knowledge-driven method, with a F1-measure of 62.96% (considering the reduced temporal graph) or 70.22% for extraction of temporal links. The method developed consisted of initial rule-based identification and classification components which utilised contextual lexico-syntactic cues for inter-sentence links, string similarity for co-reference links, and subsequently a temporal closure component to calculate transitive relations of the extracted links. In a case study of survivors of childhood central nervous system tumours (medulloblastoma), qualitative evaluation showed that we were able to capture specific trends part of patient journeys. An overall quantitative evaluation score (average precision and recall) of 94-100% for individual and 97% for aggregated patient journeys were also achieved. Hence, indicating that text mining methods can be used to identify, extract and temporally organise key clinical concepts that make up a patient’s journey. We also presented an analyses of healthcare narratives, specifically exploring the content of clinical and patient narratives by using methods developed to extract patient journeys. We found that health-related quality of life concepts are more common in patient narrative, while clinical concepts (e.g., medical problems, treatments, tests) are more prevalent in clinical narratives. In addition, while both aggregated sets of narratives contain all investigated concepts; clinical narratives contain, proportionally, more health-related quality of life concepts than clinical concepts found in patient narratives. These results demonstrate that automated concept extraction, in particular health-related quality of life, as part of standard clinical practice is feasible. The proposed method presented herein demonstrated that text mining methods can be efficiently used to identify, extract and temporally organise key clinical concepts that make up a patient’s journey in a healthcare system. Automated reconstruction of patient journeys can potentially be of value for clinical practitioners and researchers, to aid large scale analyses of implemented care pathways, and subsequently help monitor, compare, develop and adjust clinical guidelines both in the areas of chronic diseases where there is plenty of data and rare conditions where potentially there are no established guidelines.

This thesis explores the role of the nurse on an Acute Medical Admissions Unit (AMAU). AMAUs provide a dedicated area for the assessment, treatment, and subsequent transfer or discharge of patients who are medical emergencies. Despite increasing numbers of AMAUs across the UK they are an under researched area and, in particular, there is limited research that has explored the role of the nurse in the AMAU setting. Data were generated through the use of ethnography that entailed participant observation over an eighteen-month period, semi-structured interviews with a purposive sample of doctors, nurses, paramedics, and patients (n= 19), and examination of documentary evidence. Drawing on the concept of communities of practice (Wenger 1998) and the demand-control-social support model of occupational stress (Baker et al., 1996) the key themes of the study were identified as: The AMAU nurse's role in co-ordinating patients ' transition; Professional skills and attributes o f the AMAU nurse; 7 love the buzz': the AMAU nurses' work place stresses and balances; and Organisational constraints and practice boundaries for AMA U nursing The findings from the study indicate that a key aspect of the AMAU nurse's role was the facilitation of rapid patient transition. In addition the study has identified the distinctive and locally negotiated working practices developed by the nurses to coordinate this transition. Another important claim arising from this study was the identification of this nursing role as an evolved construction within a community of practice. This study makes a significant contribution to the limited body of knowledge regarding AMAU nursing practice by aiding understanding of the complexity of this nursing role. Additionally, the application of the concept of community of practice provides a unique perspective and insight into this under explored role. Recommendations are offered for practice, education, management and future research.

The original contributions of this thesis enhance the field of healthcare improvement by providing new constructs for the conduct of patient journey modeling projects. More importantly the major contributions of this research provide practical tools that can be used by healthcare staff with little or no previous process improvement experience to improve the consistency and quality of outcomes for patient journey redesign initiatives. Significant research contributions are made in five areas: 1. Provision of a domain specific improvement architecture; 2. An assessment framework for process modelling tools; 3. A Multi-dimensional Patient Journey Modeling Conceptual Design; 4. A new healthcare specific patient journey modelling tool; 5. Improved technology support for patient journey modelling. The Patient Journey Modeling Meta-Methodology (PJM3) delivers an overarching architecture on which to base healthcare improvement initiatives well beyond any currently in existence. PJM3 allows for the definition of the healthcare provider strategy, in the form of Balanced Scorecards, and for these to be explicitly linked with operational process changes. PJM3 also accommodates the inclusion of relevant legislation/policies and accreditation requirements, practice guidelines and patient needs into the process modeling activities. The research concepts and tools presented in this work are demonstrated via their application in an Australian primary care maternity service.